The Impact of Cancer Status on Anxiety in Prostate Cancer Patients: A Network Analysis
Bibliographic record
Abstract
Prostate cancer (PCa) patients often also suffer from comorbid anxiety, which can impede treatment efficacy as well as be intrinsically unpleasant. Identification of the associations between particular symptoms of anxiety that are most likely to occur at different points in the PCa diagnosis-treatment journey can inform anxiety treatment choices and potentially influence their overall treatment outcomes. Although simple correlational analyses and ANOVA models of data analysis have been used to address this issue, the possibility of confounds due to the inter-relationships between other anxiety symptoms argues for the use of network analysis, which calculates each symptom-symptom connection while also taking into account the entire range of symptom relationships. Responses to the GAD-10 self-report scale for Generalised Anxiety Disorder were collected from 415 PCa patients who were grouped according to whether (1) their PCa was just diagnosed and undergoing initial treatment; (2) their cancer was in remission; or (3) their cancer was recurring after initial treatment. The results of the network analysis indicated several areas where clinically relevant differences were present between the three PCa groups, but caution was applied to the results of statistical tests due to unequal sample sizes. Individual GAD symptom-symptom association differences are discussed in terms of their implications for directed and individualised anxiety-management treatment models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".